A Genetic Programming Ecosystem

Published

April 1, 2001

Author(s)

J E. Devaney, John G. Hagedorn, O P. Nicolas, G Garg, A Samson, Martial Michel

Abstract

Algorithms are needed in every aspect of parallel computing. Genetic Programmingis an evolutionary technique for automating the design of algorithms throughiterative steps of mutation and crossover operations on an initial population of randomly generated computer programs. This paper describes a parallel genetic programming (GP) system inspired by the symbiogenesis model of evolution, wherein new organisms are generated through the absorption of different life-forms in addition to the usual mutation and crossover operations. Different organisms are expressed in this GP system through multiple program representations. Two program representations considered in this paper are the procedural representation (PR) and the tree representation (TR). Populations of these representations evolve separately. Individuals in each population migrate to the other and participate in evolution via representation change algorithms. Parallelism is achieved through use of the AutoMap/AutoLink MPI library. The differences in the locality properties of the representations serve as a source of new ideas for creating the final algorithm.

Proceedings Title

International Parallel and Distributed Processing Symposium | 15th | IPDPS 2001, Workshop on Biologically | IEEE

Conference Dates

April 1, 2001

Conference Location

Undefined

Conference Title

IEEE International Parallel and Distributed Processing Symposium

Pub Type

Conferences

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Keywords

discovery science, distributed processing, immersive environments, IMPI, interoperable MPI, message passing interface, MPI, parallel processing, scientific visualization

Citation

Devaney, J. , Hagedorn, J. , Nicolas, O. , Garg, G. , Samson, A. and Michel, M. (2001), A Genetic Programming Ecosystem, International Parallel and Distributed Processing Symposium | 15th | IPDPS 2001, Workshop on Biologically | IEEE, Undefined, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=151507 (Accessed October 31, 2024)